1. Field of the Invention
The invention relates generally to an information relevance display method, program, storage medium and apparatus which perform network display of commonality between pieces of element information by utilizing relevance according to attributes possessed by element information such as gene information, and more specifically, to an information relevance display method, program, storage medium and apparatus which display the relevance with the network in which elements are connected as nodes by edges of common attributes.
2. Description of the Related Art
Traditionally, in the field of bioinformatics, starting with sequence analysis of human genome, the genome sequence analysis of animals other than human, plants, micro-organisms and the like is currently underway, and these genome sequence data is registered in databases which are administered by public institutions of countries, published through the internet to all over the world and utilized extensively. Usually, in bioresearch, properties for each gene, such as what functions it has, where in the body it works and what proteins it generates, are examined and stored in public sites and local sites all over the world. In this case, the genes which have the same properties are considered to play the same role, and researchers strive to predict what role the newly discovered genes play and to identify genes associated to disease, as they repeat experiments based on these information analysis and analysis.
However, in these traditional genome researches, for genes disclosed as the genome sequence data, researchers examined in what way the relevance (commonality) of each data exists by looking into individual data, and problem exists in that it is difficult for analysts to focus attention on the relevance, especially the commonality, of certain gene, as they cover huge volumes of data, and to look into and intuitively capture characteristics thereof. These problems also exit in the data analysis in various fields in which it is needed to intuitively capture the commonality of individual data and other data, covering huge volumes of data other than bioinformatics.